The present invention relates to the field of positron imaging, and more particularly to the reconstruction of data acquired in positron emission tomography (PET). It also finds application to the reconstruction of data acquired in single photon emission computed tomography (SPECT), computed tomography (CT), and other applications where the reconstruction of acquired data is required.
Positron emission tomography (PET) is a branch of nuclear medicine in which a positron-emitting radiopharmaceutical such as 18F-fluorodeoxyglucose (FDG) is introduced into the body of a patient. As the radiopharmaceutical decays, positrons are generated. More specifically, each of a plurality of positrons reacts with an electron in what is known as a positron annihilation event, thereby generating a coincident pair of 511 keV gamma rays which travel in opposite directions along a line of response (LOR). A gamma ray pair detected within a coincidence time is ordinarily recorded by the PET scanner as an annihilation event. In time of flight (TOF) imaging, the time within the coincidence interval at which each gamma ray in the coincident pair is detected is further measured. The time of flight information provides an indication of the location of the detected event along the LOR.
In three-dimensional PET, a four-dimensional projection sinogramn or event list is acquired. In many cases, the region of interest in the object under examination has a longer longitudinal dimension than the scanner's axial field of view. Thus, data is often acquired in a frame-based or step and shoot mode in which data is acquired with the object support and hence the object located at each of a plurality of discrete longitudinal positions. In a continuous mode, the object support is moved substantially continuously during the acquisition.
Data from the scan is used to reconstruct volumetric data indicative of the distribution of the radionuclide in the object. Reconstruction is typically performed using statistical (iterative) or analytical reconstruction algorithms. Iterative reconstruction techniques include the maximum likelihood expectation maximization (ML-EM), ordered subsets expectation maximization (OS-EM), rescaled block iterative expectation maximization (RBI-EM), and row action maximization likelihood (RAMLA) techniques. See Shepp and Vardi, Maximum Likelihood Reconstruction for Emission Tomography, IEEE Trans. Med. Imaging vol. MI-2, pp 113-122 (1982); Hudson and Larkin, Accelerated Image Reconstruction Using Ordered Subsets of Projection Data, IEEE Trans. Med. Imaging vol. 13, no. 4, pp 601-609 (1994); Byrne, Accelerating the EMML Algorithm and Related Iterative Algorithms by Rescaled Block-Iterative Methods, IEEE Trans. Image Processing, vol. 7, no. 1 pp. 100-109 (1998); Brown and DePierro, A Row-Action Alternative to the EM Algorithm for Maximizing Likelihoods in Emission Tomography, IEEE Trans. Med. Imaging vol. 15, no. 5, pp 687-699 (1996). While iterative methods can provide a superior reconstruction, they are as a rule more complex, computationally more expensive, and relatively more time consuming.
As a result, reconstruction time can be a key factor in the performance of PET imaging systems. This is especially true when iterative reconstruction techniques are used, and even more so when data is acquired with the object located at multiple longitudinal positions.
One technique for accelerating the convergence of an iterative reconstruction and thereby reducing reconstruction time has been the use of subsets. As a practical matter, however, there is a limit to the number of subsets that can be used without sacrificing reconstruction quality. See, e.g., PET Image Reconstruction, Alessio and Kinahan, Department of Radiology, University of Washington, Seattle, Wash., www.depts.washington.edu/nucmed/IRL/pims/2005/05/25/alessioPETRecon.pdf.
To reduce reconstruction time in frame-based imaging, data from each frame or longitudinal position has been provided to different processors in sequence. Thus, data from the first frame has been provided to a first processor; data from the second frame has been provided to a second processor, and so on. Once each processor has processed data, the first processor receives additional data from yet another frame, and the process is repeated. A particular drawback to this technique, however, is that one or more processors can remain idle while waiting for data. Moreover, such a technique is not well suited to continuous acquisition modes, nor is it well suited to concurrent image acquisition and reconstruction.
Still another technique for reducing reconstruction time in iterative reconstruction has been the use of a parallel processing architecture. See, e.g., On Parallelizing the EM Reconstruction Algorithm for PET Image Reconstruction, Chen and Lee, IEEE Transactions on Parallel and Distributed Systems, Vol. 5, No. 8, 1994. However, the use of additional processing elements increases system cost and complexity. Accordingly, it is desirable that the available processing resources be used relatively efficiently.